Latest publication! ‘Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions’ has been published in IEEE Internet of Things Journal!

This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications.

paper link: https://ieeexplore.ieee.org/document/10304218